Photogrammetry is the science of making measurements from photographs and for recovering the exact positions of surface points Photogrammetric computational methods draw upon optics, mathematics and projective geometry.
The U.S. Army Corps of Engineers (USACE) is conducting advanced research to meet the challenge of using photogrammetric techniques to transform millions of terabytes of Full Motion Video (FMV) data from unmanned vehicles, wireless cameras and other sensors into usable 2-D and 3-D maps and models
To apply photogrammetric computational models, FMV data gathered from airplanes, satellites, and other sources must be parsed into still frame images. The still frame images contain coordinates and meta data which are extracted for processing by computational model. The exterior orientation of the camera or sensor defines its location in space and its view direction. The inner orientation defines the geometric parameters of the imaging process. The focal length of the imaging lens is a component of these geometric parameters.
By feeding the measurements from each individually processed image frame into a computational model, it is possible to rapidly and accurately estimate 3-D relative motions and other measurements for each frame. It currently is not possible toto produce accurate 3-D models in real time, on a consistent basis, because of limitations inherent in the frame selection process. Frame selection is costly and time-consuming process that is currently performed by manually by human technicians.
The current state of art requires human intervention to filter relevant the frames which contain target objects from frames are irrelevant and should be excluded from computational analysis
There is an unmet need for intelligent, automated technologies which can rapidly select relevant image frames for processing from vast amounts of video data.
There is an unmet need for automated technologies which can detect and filter frames which are distorted or otherwise of insufficient quality for accurate processing.
There is a further unmet need for a fully automated method for processing FMV data that can address anomalies in acquired data and filter image frames which are unsuitable for processing in real-time.